About Me
Co-founder of MythyaVerse, building AI products and automation systems from real customer workflows.
Combines product ownership, solution architecture, and hands-on engineering across LLM systems, RAG, agents, cloud deployment, Kubernetes, OCR, computer vision, evaluation, and AI developer tooling.
Double Ph.D. in computational cognitive neuroscience and machine learning, with published work across EEG modeling, cognitive workload classification, and computational models of depression.
Experience
Co-founded MythyaVerse, an AI product studio building domain-specific AI products and production automation systems.
Own product direction across discovery, workflow mapping, solution architecture, cloud deployment, technical delivery, monitoring, and iteration.
Built and commercialized VRecruit, an AI recruiting platform for screening, coding rounds, AI interviews, scheduling, and candidate comparison.
Built Sloth, a healthcare AI product integrated with PointClickCare workflows for US care teams.
Developing MazeLedger for fintech and exchange workflows across operations, compliance, finance, and trading support.
Shaping education products across upskilling, student support, teacher workflows, assessment, feedback loops, and conceptual remediation.
Worked with public ecosystem and enterprise brands including ZebPay, Extramarks, IIT Kanpur, boAt, Hyundai, Tata, India Post, and Reliance Insurance.
Core expertise
- Production AI systems: workflow discovery, solution architecture, production system design, cloud deployment, scaling, monitoring, and iteration from customer-facing implementations.
- Domain workflows: healthcare operations, recruiting, education platforms, fintech and exchange operations, customer support, marketing, compliance, finance, and internal automation.
- AI, cloud, and platform engineering: LLM/VLM systems, RAG, agents, OCR, document AI, computer vision, clinical AI, model tuning, Docker, Kubernetes, cloud infrastructure, deployment, monitoring, and evaluation.
Selected products / systems
- Healthcare systems: clinical assistants, PointClickCare integration, ECG modeling, dental caries identification, and segmentation-based computer vision.
- Education systems: AI-human course delivery, 24x7 student assistants, dynamic difficulty, teacher workflows, feedback loops, and remediation.
- Fintech and exchange systems: operations, customer care, marketing, trading support, compliance, and finance workflows.
- Enterprise systems: RAG copilots, agentic workflows, OCR/document AI, internal automation, cloud-native deployment, Kubernetes-based services, and applied LLM/VLM systems.
Technical skills
AI systems
LLM/VLM systems, RAG, vector retrieval, prompt design, structured outputs, agents, tool calling, evaluation, OCR, document AI, and model tuning.
AI developer tooling
Codex, Claude Code, MCP integrations, plugins, orchestration, and agent runtimes.
Programming, cloud, and platform
Python, C++, JavaScript / TypeScript, PyTorch, TensorFlow, scikit-learn, FastAPI, SQL, Docker, Kubernetes, Linux, GitHub Actions, CI/CD, solution architecture, model serving, cloud deployment, and monitoring.
Ph.D. research
University of Groningen and IIT Roorkee
Computational Cognitive Neuroscience and Machine Learning
2018 - 2025
Completed research on modeling and quantifying rumination and depression using behavioral experiments, computational cognitive models, EEG, and machine learning.
Designed cross-national behavioral studies, neurophysiological experiments, and computational models to identify mechanisms underlying depressive cognition.
Built EEG classification pipelines for cognitive workload, attention states, and demographic prediction using deep learning and functional connectivity features.
Developed cognitive modeling work in ACT-R and instance-based learning, connecting human memory and decision-making theory with practical ML evaluation.
Education
Ph.D.
University of Groningen
2018 - 2025
Ph.D.
IIT Roorkee
2018 - 2025
M.Tech.
NIT Hamirpur
2014 - 2016
CGPA 9.18
B.Tech.
NIT Uttarakhand
2010 - 2014
CGPA 8.93
Selected research
Modeling the Ruminative Mind
Ph.D. thesis, University of Groningen and IIT Roorkee, 2025
Modeling Effects of Rumination on Free Recall Using ACT-R
Topics in Cognitive Science, 2024
Efficacy of Transformer Networks for Classification of EEG Data
Biomedical Signal Processing and Control, 2024
Raw EEG Cognitive Workload Classification using Directed Functional Connectivity and Deep Learning
Big Data, 2023
Subject-Specific Cognitive Workload Classification using EEG-Based Functional Connectivity and Deep Learning
Read
Sensors, 2021
IEEE Access, 2021
IEEE Sensors Journal, 2018
Research engagements
- Visiting PhD Researcher, University of Groningen (2021): cross-national work on reward learning, spontaneous thought, and depression mechanisms.
- Research Intern, INMAS-DRDO (2019): EEG-based depression studies and meditation-effect analysis on clinical cohorts.
- Research Intern, ACS Lab IIT Mandi (2019): PyIBL vs Q-learning benchmarking in dynamic environments (later published).
- Research Visit, Osaka Prefecture University (2018): BCI and deep learning collaboration under JST Sakura program.
Volunteer / academic service
Invited speaker
Winter School on Cognitive Modeling, IIT Mandi
2021, 2022
Teaching Assistant
Deep Learning Track, Neuromatch Academy
2022
Peer review
Nature Scientific Reports, Springer Nature Computer Science, IET Biometrics.
Taught English to village students in Sarath, Jharkhand.